Effort estimation of open source Android projects via transaction analysis

Transactions have been used in many software sizing methods to measure software functional size and provide a basis for effort estimation. To further investigate the effects that transactions have on software functional size and more accurately estimate project effort using the transactional information, in this paper, we propose a method to identify transactions, as well as their complexity attributes that are influential to software functional size, directly from the source code of Android projects using static and dynamic analyses.

[1]  Jian Feng Cui,et al.  Applying agglomerative hierarchical clustering algorithms to component identification for legacy systems , 2011, Inf. Softw. Technol..

[2]  James Allan,et al.  A comparison of statistical significance tests for information retrieval evaluation , 2007, CIKM '07.

[3]  Barry W. Boehm,et al.  A light-weight incremental effort estimation model for use case driven projects , 2017, 2017 IEEE 28th Annual Software Technology Conference (STC).

[4]  Giuseppe Scanniello,et al.  On the Use of Requirements Measures to Predict Software Project and Product Measures in the Context of Android Mobile Apps: A Preliminary Study , 2015, 2015 41st Euromicro Conference on Software Engineering and Advanced Applications.

[5]  Barry W. Boehm,et al.  Process-Driven Incremental Effort Estimation , 2019, 2019 IEEE/ACM International Conference on Software and System Processes (ICSSP).

[6]  Filomena Ferrucci,et al.  Investigating Functional and Code Size Measures for Mobile Applications , 2015, EUROMICRO-SEAA.

[7]  Anureet Kaur,et al.  Effort Estimation for Mobile Applications Using Use Case Point (UCP) , 2019 .

[8]  Carol Dekkers,et al.  Using''Backfiring''to accurately size software: More Wishful Thinking than Science , 2000 .

[9]  Laudson Silva de Souza,et al.  ESTIMATING THE EFFORT OF MOBILE APPLICATION DEVELOPMENT , 2014, CSE 2014.

[10]  Huibiao Zhu,et al.  RunDroid: recovering execution call graphs for Android applications , 2017, ESEC/SIGSOFT FSE.

[11]  Ivar Jacobson,et al.  Object-oriented software engineering - a use case driven approach , 1993, TOOLS.

[12]  Tore Dybå,et al.  A systematic review of effect size in software engineering experiments , 2007, Inf. Softw. Technol..

[13]  Barry W. Boehm,et al.  Poster: UMLx: A UML Diagram Analytic Tool for Software Management Decisions , 2018, 2018 IEEE/ACM 40th International Conference on Software Engineering: Companion (ICSE-Companion).

[14]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[15]  Shinji Kusumoto,et al.  Function point measurement tool for UML design specification , 1999, Proceedings Sixth International Software Metrics Symposium (Cat. No.PR00403).

[16]  Ellis Horowitz,et al.  Software Cost Estimation with COCOMO II , 2000 .

[17]  A. Stuart,et al.  Kendall's Advanced Theory of Statistics, Volume 1: Distribution Theory , 1988 .

[18]  Barry W. Boehm,et al.  Detailed Use Case Points (DUCPs): A Size Metric Automatically Countable from Sequence and Class Diagrams , 2018, 2018 IEEE/ACM 10th International Workshop on Modelling in Software Engineering (MiSE).

[19]  M. Kendall,et al.  Kendall's advanced theory of statistics , 1995 .

[20]  Jacques Klein,et al.  FlowDroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android apps , 2014, PLDI.

[21]  Arie van Deursen,et al.  Identifying objects using cluster and concept analysis , 1999, Proceedings of the 1999 International Conference on Software Engineering (IEEE Cat. No.99CB37002).

[22]  Tammy Preuss Mobile Applications, Function Points and Cost Estimating , 2013 .

[23]  Yan Wang,et al.  Static window transition graphs for Android , 2018, Automated Software Engineering.

[24]  Genny Tortora,et al.  Class point: an approach for the size estimation of object-oriented systems , 2005, IEEE Transactions on Software Engineering.

[25]  Richard C. Holt,et al.  ACCD: an algorithm for comprehension-driven clustering , 2000, Proceedings Seventh Working Conference on Reverse Engineering.

[26]  Baowen Xu,et al.  Software effort estimation based on open source projects: Case study of Github , 2017, Inf. Softw. Technol..

[27]  Dick B. Simmons,et al.  An Effort Estimation by UML Points in Early Stage of Software Development , 2006, Software Engineering Research and Practice.

[28]  Tim Menzies,et al.  Studies of Confidence in Software Cost Estimation Research Based on the Criterions MMRE and PRED Dan Port ( UHawaii ) , 2009 .

[29]  Barry W. Boehm,et al.  Calibrating use case points using bayesian analysis , 2018, ESEM.

[30]  C. Dunnett A Multiple Comparison Procedure for Comparing Several Treatments with a Control , 1955 .

[31]  Harold S. van Heeringen,et al.  Measure the Functional Size of a Mobile App: Using the COSMIC Functional Size Measurement Method , 2014, 2014 Joint Conference of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement.

[32]  Gustav Karner,et al.  Resource Estimation for Objectory Projects , 2010 .

[33]  Jesús M. González-Barahona,et al.  Estimating development effort in Free/Open source software projects by mining software repositories: a case study of OpenStack , 2014, MSR 2014.

[34]  Eric Bodden,et al.  Instrumenting Android and Java Applications as Easy as abc , 2013, RV.